Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations2111
Missing cells0
Missing cells (%)0.0%
Duplicate rows9
Duplicate rows (%)0.4%
Total size in memory206.3 KiB
Average record size in memory100.1 B

Variable types

Categorical8
Numeric9

Alerts

Dataset has 9 (0.4%) duplicate rowsDuplicates
Gender is highly overall correlated with Height and 1 other fieldsHigh correlation
Height is highly overall correlated with GenderHigh correlation
Obesity is highly overall correlated with Gender and 1 other fieldsHigh correlation
Weight is highly overall correlated with family_historyHigh correlation
family_history is highly overall correlated with Obesity and 1 other fieldsHigh correlation
CAEC is highly imbalanced (58.1%) Imbalance
SMOKE is highly imbalanced (85.4%) Imbalance
SCC is highly imbalanced (73.3%) Imbalance
MTRANS is highly imbalanced (57.1%) Imbalance
FAF has 411 (19.5%) zeros Zeros
TUE has 557 (26.4%) zeros Zeros
Obesity has 272 (12.9%) zeros Zeros

Reproduction

Analysis started2025-01-21 14:37:24.041558
Analysis finished2025-01-21 14:37:43.686812
Duration19.65 seconds
Software versionydata-profiling vv4.12.1
Download configurationconfig.json

Variables

Gender
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size119.7 KiB
1
1068 
0
1043 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Length

2025-01-21T20:07:43.853985image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T20:07:44.075979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Most occurring characters

ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1068
50.6%
0 1043
49.4%

Age
Real number (ℝ)

Distinct1402
Distinct (%)66.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.3126
Minimum14
Maximum61
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-01-21T20:07:44.324979image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile17.891428
Q119.947192
median22.77789
Q326
95-th percentile38.09807
Maximum61
Range47
Interquartile range (IQR)6.052808

Descriptive statistics

Standard deviation6.3459683
Coefficient of variation (CV)0.26101562
Kurtosis2.826389
Mean24.3126
Median Absolute Deviation (MAD)3.22211
Skewness1.5291004
Sum51323.898
Variance40.271313
MonotonicityNot monotonic
2025-01-21T20:07:44.630527image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 128
 
6.1%
26 101
 
4.8%
21 96
 
4.5%
23 89
 
4.2%
19 59
 
2.8%
20 48
 
2.3%
22 39
 
1.8%
17 30
 
1.4%
24 18
 
0.9%
25 16
 
0.8%
Other values (1392) 1487
70.4%
ValueCountFrequency (%)
14 1
 
< 0.1%
15 1
 
< 0.1%
16 9
0.4%
16.093234 1
 
< 0.1%
16.129279 1
 
< 0.1%
16.172992 1
 
< 0.1%
16.198153 1
 
< 0.1%
16.240576 1
 
< 0.1%
16.270434 1
 
< 0.1%
16.30687 2
 
0.1%
ValueCountFrequency (%)
61 1
< 0.1%
56 1
< 0.1%
55.24625 1
< 0.1%
55.137881 1
< 0.1%
55.022494 1
< 0.1%
55 2
0.1%
52 1
< 0.1%
51 1
< 0.1%
50.832559 1
< 0.1%
47.7061 1
< 0.1%

Height
Real number (ℝ)

High correlation 

Distinct1574
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7016774
Minimum1.45
Maximum1.98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-01-21T20:07:45.128674image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1.45
5-th percentile1.5482905
Q11.63
median1.700499
Q31.768464
95-th percentile1.85
Maximum1.98
Range0.53
Interquartile range (IQR)0.138464

Descriptive statistics

Standard deviation0.09330482
Coefficient of variation (CV)0.054831088
Kurtosis-0.56294889
Mean1.7016774
Median Absolute Deviation (MAD)0.069769
Skewness-0.012854646
Sum3592.2409
Variance0.0087057894
MonotonicityNot monotonic
2025-01-21T20:07:45.452671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.7 60
 
2.8%
1.65 50
 
2.4%
1.6 43
 
2.0%
1.75 39
 
1.8%
1.62 36
 
1.7%
1.8 28
 
1.3%
1.72 19
 
0.9%
1.63 17
 
0.8%
1.67 16
 
0.8%
1.78 15
 
0.7%
Other values (1564) 1788
84.7%
ValueCountFrequency (%)
1.45 1
 
< 0.1%
1.456346 1
 
< 0.1%
1.48 1
 
< 0.1%
1.481682 1
 
< 0.1%
1.483284 1
 
< 0.1%
1.486484 1
 
< 0.1%
1.489409 1
 
< 0.1%
1.491441 1
 
< 0.1%
1.498561 1
 
< 0.1%
1.5 13
0.6%
ValueCountFrequency (%)
1.98 1
< 0.1%
1.975663 1
< 0.1%
1.947406 1
< 0.1%
1.942725 1
< 0.1%
1.931263 1
< 0.1%
1.930416 1
< 0.1%
1.93 2
0.1%
1.92 1
< 0.1%
1.919543 1
< 0.1%
1.918859 1
< 0.1%

Weight
Real number (ℝ)

High correlation 

Distinct1525
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86.586058
Minimum39
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-01-21T20:07:45.765627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile48.5
Q165.473343
median83
Q3107.43068
95-th percentile131.91615
Maximum173
Range134
Interquartile range (IQR)41.957339

Descriptive statistics

Standard deviation26.191172
Coefficient of variation (CV)0.30248717
Kurtosis-0.69989816
Mean86.586058
Median Absolute Deviation (MAD)21.735215
Skewness0.2554105
Sum182783.17
Variance685.97748
MonotonicityNot monotonic
2025-01-21T20:07:46.074632image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80 59
 
2.8%
70 43
 
2.0%
50 42
 
2.0%
75 40
 
1.9%
60 37
 
1.8%
65 26
 
1.2%
42 22
 
1.0%
90 20
 
0.9%
78 19
 
0.9%
45 18
 
0.9%
Other values (1515) 1785
84.6%
ValueCountFrequency (%)
39 1
< 0.1%
39.101805 1
< 0.1%
39.371523 1
< 0.1%
39.695295 1
< 0.1%
39.850137 1
< 0.1%
40 1
< 0.1%
40.202773 1
< 0.1%
40.343463 1
< 0.1%
41.220175 1
< 0.1%
41.268597 1
< 0.1%
ValueCountFrequency (%)
173 1
< 0.1%
165.057269 1
< 0.1%
160.935351 1
< 0.1%
160.639405 1
< 0.1%
155.872093 1
< 0.1%
155.242672 1
< 0.1%
154.618446 1
< 0.1%
153.959945 1
< 0.1%
153.149491 1
< 0.1%
152.720545 1
< 0.1%

family_history
Categorical

High correlation 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size119.7 KiB
1
1726 
0
385 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Length

2025-01-21T20:07:46.334627image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T20:07:46.542628image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Most occurring characters

ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1726
81.8%
0 385
 
18.2%

FAVC
Categorical

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size119.7 KiB
1
1866 
0
245 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Length

2025-01-21T20:07:46.754653image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T20:07:46.961573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Most occurring characters

ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 1866
88.4%
0 245
 
11.6%

FCVC
Real number (ℝ)

Distinct810
Distinct (%)38.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4190431
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-01-21T20:07:47.220592image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.5232145
Q12
median2.385502
Q33
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.53392658
Coefficient of variation (CV)0.2207181
Kurtosis-0.6375459
Mean2.4190431
Median Absolute Deviation (MAD)0.385502
Skewness-0.43290583
Sum5106.5999
Variance0.28507759
MonotonicityNot monotonic
2025-01-21T20:07:47.554750image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 652
30.9%
2 600
28.4%
1 33
 
1.6%
2.823179 2
 
0.1%
2.21498 2
 
0.1%
2.795086 2
 
0.1%
2.442536 2
 
0.1%
2.81646 2
 
0.1%
2.938031 2
 
0.1%
2.954996 2
 
0.1%
Other values (800) 812
38.5%
ValueCountFrequency (%)
1 33
1.6%
1.003566 1
 
< 0.1%
1.005578 1
 
< 0.1%
1.00876 1
 
< 0.1%
1.031149 1
 
< 0.1%
1.036159 1
 
< 0.1%
1.036414 1
 
< 0.1%
1.052699 1
 
< 0.1%
1.053534 1
 
< 0.1%
1.063449 1
 
< 0.1%
ValueCountFrequency (%)
3 652
30.9%
2.998441 1
 
< 0.1%
2.997951 1
 
< 0.1%
2.997524 1
 
< 0.1%
2.996717 1
 
< 0.1%
2.996186 1
 
< 0.1%
2.995599 1
 
< 0.1%
2.99448 1
 
< 0.1%
2.992329 1
 
< 0.1%
2.992205 1
 
< 0.1%

NCP
Real number (ℝ)

Distinct635
Distinct (%)30.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.685628
Minimum1
Maximum4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-01-21T20:07:47.863131image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12.658738
median3
Q33
95-th percentile3.750881
Maximum4
Range3
Interquartile range (IQR)0.341262

Descriptive statistics

Standard deviation0.77803865
Coefficient of variation (CV)0.28970454
Kurtosis0.38552662
Mean2.685628
Median Absolute Deviation (MAD)0
Skewness-1.1070973
Sum5669.3608
Variance0.60534414
MonotonicityNot monotonic
2025-01-21T20:07:48.303845image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 1203
57.0%
1 199
 
9.4%
4 69
 
3.3%
2.77684 2
 
0.1%
3.985442 2
 
0.1%
1.73762 2
 
0.1%
1.894384 2
 
0.1%
1.104642 2
 
0.1%
2.644692 2
 
0.1%
3.559841 2
 
0.1%
Other values (625) 626
29.7%
ValueCountFrequency (%)
1 199
9.4%
1.000283 1
 
< 0.1%
1.000414 1
 
< 0.1%
1.00061 1
 
< 0.1%
1.001383 1
 
< 0.1%
1.001542 1
 
< 0.1%
1.001633 1
 
< 0.1%
1.005391 1
 
< 0.1%
1.009426 1
 
< 0.1%
1.010319 1
 
< 0.1%
ValueCountFrequency (%)
4 69
3.3%
3.999591 1
 
< 0.1%
3.998766 1
 
< 0.1%
3.998618 1
 
< 0.1%
3.995957 1
 
< 0.1%
3.995147 1
 
< 0.1%
3.994588 1
 
< 0.1%
3.990925 1
 
< 0.1%
3.98955 1
 
< 0.1%
3.989492 1
 
< 0.1%

CAEC
Categorical

Imbalance 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size119.7 KiB
2
1765 
1
242 
0
 
53
3
 
51

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
2 1765
83.6%
1 242
 
11.5%
0 53
 
2.5%
3 51
 
2.4%

Length

2025-01-21T20:07:48.587923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T20:07:48.820923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 1765
83.6%
1 242
 
11.5%
0 53
 
2.5%
3 51
 
2.4%

Most occurring characters

ValueCountFrequency (%)
2 1765
83.6%
1 242
 
11.5%
0 53
 
2.5%
3 51
 
2.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1765
83.6%
1 242
 
11.5%
0 53
 
2.5%
3 51
 
2.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1765
83.6%
1 242
 
11.5%
0 53
 
2.5%
3 51
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1765
83.6%
1 242
 
11.5%
0 53
 
2.5%
3 51
 
2.4%

SMOKE
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size119.7 KiB
0
2067 
1
 
44

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Length

2025-01-21T20:07:49.071927image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T20:07:49.295918image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Most occurring characters

ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2067
97.9%
1 44
 
2.1%

CH2O
Real number (ℝ)

Distinct1268
Distinct (%)60.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0080114
Minimum1
Maximum3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-01-21T20:07:49.574931image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5848125
median2
Q32.47742
95-th percentile3
Maximum3
Range2
Interquartile range (IQR)0.8926075

Descriptive statistics

Standard deviation0.61295345
Coefficient of variation (CV)0.30525397
Kurtosis-0.87939461
Mean2.0080114
Median Absolute Deviation (MAD)0.452986
Skewness-0.10491164
Sum4238.9121
Variance0.37571193
MonotonicityNot monotonic
2025-01-21T20:07:49.922982image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 448
 
21.2%
1 211
 
10.0%
3 162
 
7.7%
2.825629 3
 
0.1%
1.636326 3
 
0.1%
2.115967 2
 
0.1%
2.174248 2
 
0.1%
2.530035 2
 
0.1%
2.450069 2
 
0.1%
1.439962 2
 
0.1%
Other values (1258) 1274
60.4%
ValueCountFrequency (%)
1 211
10.0%
1.000463 1
 
< 0.1%
1.000536 1
 
< 0.1%
1.000544 1
 
< 0.1%
1.000695 1
 
< 0.1%
1.001307 1
 
< 0.1%
1.001995 1
 
< 0.1%
1.002292 1
 
< 0.1%
1.003063 1
 
< 0.1%
1.003563 1
 
< 0.1%
ValueCountFrequency (%)
3 162
7.7%
2.999495 1
 
< 0.1%
2.994515 1
 
< 0.1%
2.993448 1
 
< 0.1%
2.991671 1
 
< 0.1%
2.989389 1
 
< 0.1%
2.988771 1
 
< 0.1%
2.987718 1
 
< 0.1%
2.987406 1
 
< 0.1%
2.984323 1
 
< 0.1%

SCC
Categorical

Imbalance 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size119.7 KiB
0
2015 
1
 
96

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Length

2025-01-21T20:07:50.220742image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T20:07:50.437685image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Most occurring characters

ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 2015
95.5%
1 96
 
4.5%

FAF
Real number (ℝ)

Zeros 

Distinct1190
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0102977
Minimum0
Maximum3
Zeros411
Zeros (%)19.5%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-01-21T20:07:50.682682image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.124505
median1
Q31.6666775
95-th percentile2.677133
Maximum3
Range3
Interquartile range (IQR)1.5421725

Descriptive statistics

Standard deviation0.85059243
Coefficient of variation (CV)0.84192257
Kurtosis-0.62058776
Mean1.0102977
Median Absolute Deviation (MAD)0.804157
Skewness0.49848961
Sum2132.7384
Variance0.72350748
MonotonicityNot monotonic
2025-01-21T20:07:50.976529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 411
 
19.5%
1 234
 
11.1%
2 183
 
8.7%
3 75
 
3.6%
0.110174 2
 
0.1%
1.661556 2
 
0.1%
0.245354 2
 
0.1%
1.067817 2
 
0.1%
0.288032 2
 
0.1%
1.252472 2
 
0.1%
Other values (1180) 1196
56.7%
ValueCountFrequency (%)
0 411
19.5%
9.6 × 10-51
 
< 0.1%
0.000272 1
 
< 0.1%
0.000454 1
 
< 0.1%
0.001015 1
 
< 0.1%
0.001086 1
 
< 0.1%
0.001272 1
 
< 0.1%
0.001297 1
 
< 0.1%
0.00203 1
 
< 0.1%
0.00342 1
 
< 0.1%
ValueCountFrequency (%)
3 75
3.6%
2.999918 1
 
< 0.1%
2.998981 1
 
< 0.1%
2.971832 1
 
< 0.1%
2.939733 1
 
< 0.1%
2.936551 1
 
< 0.1%
2.931527 1
 
< 0.1%
2.892922 2
 
0.1%
2.891986 1
 
< 0.1%
2.89118 1
 
< 0.1%

TUE
Real number (ℝ)

Zeros 

Distinct1129
Distinct (%)53.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.65786592
Minimum0
Maximum2
Zeros557
Zeros (%)26.4%
Negative0
Negative (%)0.0%
Memory size16.6 KiB
2025-01-21T20:07:51.288804image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.62535
Q31
95-th percentile2
Maximum2
Range2
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.60892726
Coefficient of variation (CV)0.92560997
Kurtosis-0.5486604
Mean0.65786592
Median Absolute Deviation (MAD)0.484872
Skewness0.61850241
Sum1388.755
Variance0.37079241
MonotonicityNot monotonic
2025-01-21T20:07:51.775965image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 557
26.4%
1 292
 
13.8%
2 109
 
5.2%
0.630866 4
 
0.2%
1.119877 3
 
0.1%
0.0026 3
 
0.1%
0.009254 2
 
0.1%
0.8324 2
 
0.1%
1.36595 2
 
0.1%
0.828549 2
 
0.1%
Other values (1119) 1135
53.8%
ValueCountFrequency (%)
0 557
26.4%
7.3 × 10-51
 
< 0.1%
0.000355 1
 
< 0.1%
0.000436 1
 
< 0.1%
0.001096 1
 
< 0.1%
0.00133 1
 
< 0.1%
0.001337 1
 
< 0.1%
0.001518 1
 
< 0.1%
0.00159 1
 
< 0.1%
0.00164 1
 
< 0.1%
ValueCountFrequency (%)
2 109
5.2%
1.99219 1
 
< 0.1%
1.990617 1
 
< 0.1%
1.983678 1
 
< 0.1%
1.980875 1
 
< 0.1%
1.978043 1
 
< 0.1%
1.972926 1
 
< 0.1%
1.97117 1
 
< 0.1%
1.969507 1
 
< 0.1%
1.967259 1
 
< 0.1%

CALC
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size119.7 KiB
2
1401 
3
639 
1
 
70
0
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row3
2nd row2
3rd row1
4th row1
5th row2

Common Values

ValueCountFrequency (%)
2 1401
66.4%
3 639
30.3%
1 70
 
3.3%
0 1
 
< 0.1%

Length

2025-01-21T20:07:52.065964image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T20:07:52.282923image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
2 1401
66.4%
3 639
30.3%
1 70
 
3.3%
0 1
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
2 1401
66.4%
3 639
30.3%
1 70
 
3.3%
0 1
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 1401
66.4%
3 639
30.3%
1 70
 
3.3%
0 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 1401
66.4%
3 639
30.3%
1 70
 
3.3%
0 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 1401
66.4%
3 639
30.3%
1 70
 
3.3%
0 1
 
< 0.1%

MTRANS
Categorical

Imbalance 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size119.7 KiB
3
1580 
0
457 
4
 
56
2
 
11
1
 
7

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters2111
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row4
5th row3

Common Values

ValueCountFrequency (%)
3 1580
74.8%
0 457
 
21.6%
4 56
 
2.7%
2 11
 
0.5%
1 7
 
0.3%

Length

2025-01-21T20:07:52.512922image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-01-21T20:07:52.732622image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
ValueCountFrequency (%)
3 1580
74.8%
0 457
 
21.6%
4 56
 
2.7%
2 11
 
0.5%
1 7
 
0.3%

Most occurring characters

ValueCountFrequency (%)
3 1580
74.8%
0 457
 
21.6%
4 56
 
2.7%
2 11
 
0.5%
1 7
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
3 1580
74.8%
0 457
 
21.6%
4 56
 
2.7%
2 11
 
0.5%
1 7
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
3 1580
74.8%
0 457
 
21.6%
4 56
 
2.7%
2 11
 
0.5%
1 7
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2111
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
3 1580
74.8%
0 457
 
21.6%
4 56
 
2.7%
2 11
 
0.5%
1 7
 
0.3%

Obesity
Real number (ℝ)

High correlation  Zeros 

Distinct7
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0156324
Minimum0
Maximum6
Zeros272
Zeros (%)12.9%
Negative0
Negative (%)0.0%
Memory size8.4 KiB
2025-01-21T20:07:52.944706image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile6
Maximum6
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9520902
Coefficient of variation (CV)0.64732365
Kurtosis-1.1906523
Mean3.0156324
Median Absolute Deviation (MAD)2
Skewness0.0067544491
Sum6366
Variance3.810656
MonotonicityNot monotonic
2025-01-21T20:07:53.169621image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
2 351
16.6%
4 324
15.3%
3 297
14.1%
5 290
13.7%
6 290
13.7%
1 287
13.6%
0 272
12.9%
ValueCountFrequency (%)
0 272
12.9%
1 287
13.6%
2 351
16.6%
3 297
14.1%
4 324
15.3%
5 290
13.7%
6 290
13.7%
ValueCountFrequency (%)
6 290
13.7%
5 290
13.7%
4 324
15.3%
3 297
14.1%
2 351
16.6%
1 287
13.6%
0 272
12.9%

Interactions

2025-01-21T20:07:41.092517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:25.374348image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:27.175513image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:29.150652image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:31.049502image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:32.981631image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:35.137449image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:37.277237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:39.176662image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:41.286587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:25.541347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:27.492462image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:29.332657image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:31.255581image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:33.188737image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:35.342440image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:37.462236image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:39.376580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:41.483593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:25.733419image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:27.681385image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:29.532590image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:31.461505image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:33.390824image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:35.554514image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:37.719552image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:39.578661image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:41.679515image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:25.932402image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:27.883459image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:29.737587image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:31.665584image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:33.671055image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:35.769166image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:37.922548image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:39.791580image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:41.900175image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:26.151347image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:28.107386image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:29.958666image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:31.883529image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:33.914530image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:35.989159image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:38.139472image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:40.019589image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:42.109096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:26.341353image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:28.311646image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:30.176671image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:32.107607image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:34.147532image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:36.204162image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:38.338550image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:40.226585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:42.320096image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:26.550341image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:28.525649image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:30.390661image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:32.323608image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:34.383534image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:36.429929image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:38.554554image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:40.449585image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:42.524169image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:26.747512image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:28.720651image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:30.600669image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:32.538534image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:34.642526image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:36.641005image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:38.755593image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:40.655517image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:42.733170image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:26.966506image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:28.940573image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:30.827676image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:32.763528image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:34.925798image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:37.056237image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:38.973660image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
2025-01-21T20:07:40.880588image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/

Correlations

2025-01-21T20:07:53.375620image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
AgeCAECCALCCH2OFAFFAVCFCVCGenderHeightMTRANSNCPObesitySCCSMOKETUEWeightfamily_history
Age1.0000.1570.1630.013-0.2080.1380.0620.196-0.0030.350-0.1060.2840.1410.178-0.2980.3570.239
CAEC0.1571.0000.0980.1830.1170.1930.1300.1310.1510.0950.1670.3520.1600.0460.1330.3180.349
CALC0.1630.0981.0000.1070.1110.1370.0990.0330.0980.0950.1210.2250.0550.1040.1380.2190.012
CH2O0.0130.1830.1071.0000.1560.1950.0660.2380.2250.0900.0700.1060.1310.0750.0230.2260.233
FAF-0.2080.1170.1110.1561.0000.1560.0280.2650.3260.1150.145-0.1260.1000.0680.051-0.0440.159
FAVC0.1380.1930.1370.1950.1561.0000.0880.0600.2120.2010.0420.3280.1860.0400.1710.2930.205
FCVC0.0620.1300.0990.0660.0280.0881.0000.347-0.0560.1050.0860.0140.0940.000-0.0880.2080.121
Gender0.1960.1310.0330.2380.2650.0600.3471.0000.6160.1620.1620.5560.0980.0350.1310.3960.099
Height-0.0030.1510.0980.2250.3260.212-0.0560.6161.0000.0860.2040.0430.1740.1770.0820.4630.293
MTRANS0.3500.0950.0950.0900.1150.2010.1050.1620.0861.0000.0400.1790.0700.0000.1260.1400.118
NCP-0.1060.1670.1210.0700.1450.0420.0860.1620.2040.0401.000-0.1450.0450.0280.0870.0030.190
Obesity0.2840.3520.2250.106-0.1260.3280.0140.5560.0430.179-0.1451.0000.2350.111-0.0570.4040.540
SCC0.1410.1600.0550.1310.1000.1860.0940.0980.1740.0700.0450.2351.0000.0330.1290.2350.181
SMOKE0.1780.0460.1040.0750.0680.0400.0000.0350.1770.0000.0280.1110.0331.0000.0580.1290.000
TUE-0.2980.1330.1380.0230.0510.171-0.0880.1310.0820.1260.087-0.0570.1290.0581.000-0.0500.188
Weight0.3570.3180.2190.226-0.0440.2930.2080.3960.4630.1400.0030.4040.2350.129-0.0501.0000.557
family_history0.2390.3490.0120.2330.1590.2050.1210.0990.2930.1180.1900.5400.1810.0000.1880.5571.000

Missing values

2025-01-21T20:07:43.055192image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
A simple visualization of nullity by column.
2025-01-21T20:07:43.532185image/svg+xmlMatplotlib v3.7.5, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

GenderAgeHeightWeightfamily_historyFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSObesity
0021.01.6264.0102.03.0202.000.01.0331
1021.01.5256.0103.03.0213.013.00.0231
2123.01.8077.0102.03.0202.002.01.0131
3127.01.8087.0003.03.0202.002.00.0145
4122.01.7889.8002.01.0202.000.00.0236
5129.01.6253.0012.03.0202.000.00.0201
6023.01.5055.0113.03.0202.001.00.0221
7122.01.6453.0002.03.0202.003.00.0231
8124.01.7864.0113.03.0202.001.01.0131
9122.01.7268.0112.03.0202.001.01.0331
GenderAgeHeightWeightfamily_historyFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSObesity
2101025.7220041.628470107.218949113.03.0202.48707000.0673290.455823234
2102025.7656281.627839108.107360113.03.0202.32006800.0452460.413106234
2103021.0168491.724268133.033523113.03.0201.65061201.5376390.912457234
2104021.6823671.732383133.043941113.03.0201.61076801.5103980.931455234
2105021.2859651.726920131.335786113.03.0201.79626701.7283320.897924234
2106020.9768421.710730131.408528113.03.0201.72813901.6762690.906247234
2107021.9829421.748584133.742943113.03.0202.00513001.3413900.599270234
2108022.5240361.752206133.689352113.03.0202.05419301.4142090.646288234
2109024.3619361.739450133.346641113.03.0202.85233901.1391070.586035234
2110023.6647091.738836133.472641113.03.0202.86351301.0264520.714137234

Duplicate rows

Most frequently occurring

GenderAgeHeightWeightfamily_historyFAVCFCVCNCPCAECSMOKECH2OSCCFAFTUECALCMTRANSObesity# duplicates
7121.01.6270.0012.01.0303.001.00.023515
3021.01.5242.0013.01.0101.000.00.02304
0016.01.6658.0002.01.0201.000.01.03412
1018.01.6255.0112.03.0101.001.01.03312
2021.01.5242.0003.01.0101.000.00.02302
4022.01.6965.0112.03.0202.001.01.02312
5025.01.5755.0012.01.0202.002.00.02312
6118.01.7253.0112.03.0202.000.02.02302
8122.01.7475.0113.03.0101.001.00.03012